Quantum true random number generator

ABSTRACT

A true random number generator including a light source configured to produce randomly distributed photons, a plurality of detection channels configured to receive the randomly distributed photons produced by the light source, each detection channel including a photon sensor configured to detect a receipt of at least one photon during successive integration time-periods and generate an output signal by assigning a value for each integration time-period based on whether at least one photon was received during each integration time-period, a signal conditioning unit configured to condition the output signal of each of the plurality of detection channels and generate a conditioned output signal for each of the plurality of detection channels, and a signal processing unit configured to combine the conditioned output signals and generate a true random number based on the combination of the conditioned output signals.

CROSS REFERENCE TO PRIOR APPLICATION

This application claims priority to U.S. Provisional Application Ser.No. 62/451,239, filed on Jan. 27, 2017, which is hereby incorporated byreference in its entirety.

FIELD

Embodiments of the present invention are generally directed to a quantumtrue random number generator.

BACKGROUND

Random numbers are typically necessary in cryptography, statisticalresearch, including quantum cryptography, statistical research (MonteCarlo simulations in physics, biology, economics, etc.), randomizedalgorithms, etc. True random numbers (e.g., random numbers generatedbased on physical processes rather than by software tools) are typicallyrequired in our everyday life: mobile communications, e-mail access,online payments, cashless payments, ATMs, e-banking, Internet trade,point of sale, prepaid cards, wireless keys, general cybersecurity,distributed power grid security, etc.

True random number generators (TRNG) typically work by providing asource of truly random numbers that do not come from a mathematicalprocess, such as those used to generate pseudo random numbers in pseudorandom number generators (PRNG). Source of true randomness can be from,for example, radioactive decay (typically slow), the chaotic motion offluids (typically very slow), atmospheric noise (typically slow),quantum-based, or from other unpredictable systems that cannot beguessed by another even with access to a similar or the same device.

The generation of true random numbers at rates higher than 1 Gbit/s isan unmet market need, and according to certain recent publications, themarket is in need of stable high throughput TRNGs with throughputs of upto 30 Gbit/sec. Indeed, currently available high-throughput TRNGs arecostly and their number generation rates are limited to sub-Gbit/srates. Many of these currently available TRNGs employ various sources ofentropy (e.g., shot noise, thermal noise, and reverse biased Zenerdiodes), which are typically cheaper, less reliable, and provide lowerthroughput, while quantum optical TRNGS are able to provide very highmore robust throughput.

SUMMARY

Exemplary embodiments of the present invention can provide a true randomnumber generator. The exemplary true random number generator can includea light source configured to produce randomly distributed photons, aplurality of detection channels configured to receive the randomlydistributed photons produced by the light source, where each detectionchannel can include a photon sensor configured to detect a receipt of atleast one photon during successive integration time-periods and generatean output signal by assigning a value for each integration time-periodbased on whether at least one photon was received during eachintegration time-period, a signal conditioning unit configured tocondition the output signal of each of the plurality of detectionchannels and generate a conditioned output signal for each of theplurality of detection channels, and a signal processing unit configuredto combine the conditioned output signals and generate a true randomnumber based on the combination of the conditioned output signals.

According to certain exemplary embodiments, the light source can includea Lambertian light source and/or the successive integration time-periodscan be a function of a photon detection rate of the photon sensor.

According to certain exemplary embodiments, the signal conditioning unitcan be configured to minimize cross-talk between the plurality ofdetection channels. The signal conditioning unit can also be configuredto compare successive values in conditioning the output signal of eachof the plurality of detection channels, and the comparison of successivevalues can include comparing whether successive values are equal.Further, conditioning the output signal can include discarding thevalues when the successive values are equal and adopting the first ofthe successive values when the successive values are different.

According to certain exemplary embodiments, the signal processing unitcan be configured to combine the conditioned output signals sequentiallyin producing the true random number.

According to certain exemplary embodiments, the light source and theintegration time-periods can be configured such that a probability ofreceiving at least one photon during each integration time-period isapproximately one-half.

According to certain exemplary embodiments, the true random numbergenerator can include more than 10 detection channels, more than 30detection channels, or more than 100 detection channels. According tocertain exemplary embodiments, the true random number generator caninclude 32 detection channels.

According to certain exemplary embodiments, the true random numbergenerator can provide a throughput of at least 100 Mbit/s, a throughputof at least 1 Gbit/s, a throughput of at least 10 Gbit/s, or athroughput of at least 100 Gbit/s.

Another embodiment of the present invention can provide an exemplarymethod for generating true random numbers. The exemplary method caninclude providing a light source configured to produce randomlydistributed photons, receiving the randomly distributed photons producedby the light source using a plurality of detection channels, detecting areceipt of at least one photon during successive integrationtime-periods using a photon sensor, generating an output signal byassigning a value for each integration time-period based on whether atleast one photon was received during each integration time-period,conditioning the output signal of each of the plurality of detectionchannels and generating a conditioned output signal for each of theplurality of detection channels using the signal conditioning unit, andcombining the conditioned output signals and generating a true randomnumber based on the combination of the conditioned output signals usinga signal processing unit.

Yet another embodiment of the present invention can provide a method forrequiring a true random number. The method can include obtaining arandom number generated by a true random number generator including alight source configured to produce randomly distributed photons, aplurality of detection channels configured to receive the randomlydistributed photons produced by the light source, where each detectionchannel can include a photon sensor configured to detect a receipt of atleast one photon during successive integration time-periods and generatean output signal by assigning a value for each integration time-periodbased on whether at least one photon was received during eachintegration time-period, a signal conditioning unit configured tocondition the output signal of each of the plurality of detectionchannels and generate a conditioned output signal for each of theplurality of detection channels, and a signal processing unit configuredto combine the conditioned output signals and generate a true randomnumber based on the combination of the conditioned output signals.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an exemplary block diagram of an exemplary multi-channeltrue random number generator according to an embodiment of the presentinvention;

FIG. 2 is an exemplary schematic of an exemplary amplifier according toan embodiment of the present invention;

FIG. 3 shows an exemplary block diagram of an exemplary multi-channeltrue random number generator according to an embodiment of the presentinvention;

FIG. 4 shows an exemplary block diagram of an exemplary multi-channeltrue random number generator according to an embodiment of the presentinvention;

FIG. 5 shows exemplary results of a randomness test of an exemplarymulti-channel true random number generator according to an embodiment ofthe present invention; and

FIG. 6 shows graphs of exemplary random sequences generated by twochannels and the conditioned output of two channels of an exemplarymulti-channel true random number generator according to an embodiment ofthe present invention.

DETAILED DESCRIPTION

Exemplary embodiments of the present invention can provide a novelquantum multi-channel true random number generator (TRNG) that canprovide generation of true random numbers at the rates up to 30 Gbit/s.According to certain exemplary embodiments, true random numbers can begenerated by single photon detection to exploit fundamental randomphoton fluxes emitted by a light source. An exemplary photon detectorfor spectroscopy is described in U.S. Pat. No. 8,581,098, which isincorporated by reference herein in its entirety. For example, theexemplary TRNG can include a Lambertian light source and a multi-channelsingle photon detector. Further, the multi-channel single photondetector can be capable of detecting individual photons at the rate ashigh as 10⁸ photon/s per channel. This high photon detection rate alongwith high performance data processing and transfer electronics canenable the exemplary TRNG to provide a random number generation rate ofat least 1 Gbit/s.

For example, each detected single photon can be the source of the randomnumbers. If random bits based on photon arrival events are extracted, 2bits of raw random sequence, or 0.5 bit/sec of conditioned randomnumbers, can be obtained for each detected photon. Alternatively, otherparameters of the received photon flux can be utilized as the source ofthe random number (e.g., time intervals between consecutive photonarrivals). Utilizing other such parameters, the number of raw bits perphoton can be as high as 8 bits for 10 ns average intervals if theaccuracy of the time-to-digital conversion is in the range of 1 ps.

As described herein, embodiments of the present invention can provide ahigh throughput quantum TRNG based on the detection of single photons bya multi-channel single photon detector. A block diagram illustration ofan exemplary TRNG 100 according to an embodiment of the presentinvention is provided in FIG. 1. As shown in FIG. 1, the exemplarysystem 100 can include an adjustable light source 102 and a plurality ofchannels feeding into a signal processing unit 108, which can output thegenerated random number to random number receiving device 110. Eachchannel can include, for example, a single photo sensor 104 andinterfacing electronics 106. According to one embodiment of the presentsystem, the single photon sensor can include a single photonphotomultiplier tube (PMT). According to another embodiment of thepresent invention, the single photon sensor 104 can include, forexample, a single photon detector including a matrix or an array ofmultiple semiconductor photo detectors (e.g., silicon-photomultipliers(SiPMs), multi-pixel photon counters (MPPCs), single-photon avalanchediodes (SPAD), etc.). According to an exemplary embodiment, theexemplary TRNG can include 32 such high-speed channels. Alternatively,the multi-channel photon detector can include 8 channels, 16 channels,64 channels, or any other desired number of channels. According tocertain exemplary embodiments, the exemplary multi-channel TRNG can beimplemented, for example, employing commercial multichannelphotomultipliers having more than one channel. For example, Hamamatsu4-channel, 16-channel, 32-channel and 96-channel PMTs. Alternatively,SiPM or SPAD arrays containing from four to thousands of channels canalso be employed. TRNG random number bitrates are directly proportionalto the number of channels. Accordingly, depending on the number ofchannels used, the bitrate of the exemplary multichannel TRNG can range,for example, from 100 Mbit/s up to 100 Gbit/s and above.

In operation, the adjustable light source can simultaneously illuminatean array of multiple individual single photon sensors (e.g., an array ofseveral hundred SiPMs, SPADs or multi-channel PMT). In response to theincident photons, the single photon sensors can produce output electricpulses, which are randomly distributed in time. Additional output randomelectric pulses can be produced by the single photon sensors due totheir dark count. The output pulses of the photon sensors can be outputto interfacing electronics (IE) (e.g., comparators, time-to digitalconverters, and others). The IE can process the input signals and outputsignals, which can include transformed digital signals (e.g., logicallevels) to the signal-processing unit (HSPU) (e.g., a field programmablegate array (FPGA), microprocessor, etc.). The HSPU can count individualphotons and/or measure the time intervals between arrivals ofconsecutive photons, which can output the random numbers to a computeror other random number receiving device (e.g., various electroniccomponents and/or embedded systems, electro-optical modulators,polarizers, network cards, key management hard/form/software, etc.).Optionally, the HSPU can be designed to control and adjust comparators'thresholds individually to compensate for uneven response fromindividual single photon sensors in the array. According to certainexemplary embodiments, the IE and HSPU can be combined into a customapplication specific integrated circuit (ASIC).

According to certain exemplary embodiments, the single photon detectorcan include a 32-channel single photon detector adapted to detect up to10⁸ photon/s per channel (or up to 3.2×10⁹ photon/s total). In theexemplary TRNGs, each detected single photon can be a source of a randomnumber. With appropriate data processing, single photons can bedetected, for example, at a total rate 10⁸×32=3.2×10⁹ per second.Depending on the method of the random number extraction, 8 bits perphoton can be obtained, which can result in a 30 Gbit/sec data stream.According to certain exemplary embodiments, the exemplary detector caninclude a 32-channel PMT, 32 high bandwidth 20 dB amplifiers (one perchannel), 32 high speed low-voltage differential signal (LVDS)comparators (one per channel), a high speed FPGA photon counter, and aUSB controller. The high throughput can be obtained for example, via themultiple channels and the high detection rate for each channel. The highdetection rate for each channel can be obtained, for example, byutilizing photon detectors having a short “dead time” to enablehigh-speed photon detection. Further, high-speed detection can furtherbe achieved by adjusting the internal gain of the photon detector tocompensate for the “dead time” of the photon detector.

In response to single photons, the PMT can, for example, generate 1 nspulses, which can be amplified to ˜50 mV range by the pulse amplifiersand converted into LVDS logical levels by the comparators. The FPGA cancount the LVDS-level pulses during the integration intervals ΔT (e.g., 1μs, etc.). Then, the FPGA can build an output sequence according to anydesired formatting and interfaces (e.g., USB transfer to a PC, etc.).The system can provide, for example, up to 32 MB/sec data transfer andrecording rate.

According to exemplary embodiments, pulses produced by the PMTs can be aresult of multiple multiplication stages at the dynodes with acoefficient at each dynode. The coefficients can vary depending on manyfactors, and produced pulses corresponding to different photons can havedifferent magnitudes. The differing magnitudes, along with finitetolerance of comparator threshold, can necessitate pre-amplification ofthe pulses. However, if the amplification ratio is too high, pulses withlarge magnitudes can, for example, get into a saturation region of theamplifiers or be out of the comparator input range. In both cases, thatcan lead to broadening of the pulses thereby decreasing the maximumcount rates.

Accordingly, as shown in FIG. 2, embodiments of the present inventioncan provide optimized amplifier 200. For example, according to oneembodiment the exemplary amplifier 200 can include a two stage amplifierhaving 2 cascaded amplifiers 202 (e.g., BGA 427 amplifiers (U57 andU58)), which can provide ˜40 db of total amplification. According tocertain exemplary embodiments, each amplifier can invert the polarity ofthe pulse, such that the amplified pulses are negative. The amplifiedsignal can then go to the LVDS comparator (U59). The reference voltagecan be adjusted using potentiometer R9 from 0 to 3.3 volts. When thenegative amplified signal pulse goes to the comparator, it can generatea LVDS-level output pulse, which can go to the counters. The exemplary2-stage amplifier scheme can be especially beneficial in the case wherethe PMT voltage is reduced to have better counts at high light intensityon all channels simultaneously. In this case the PMT pulses will becomelower and higher amplification may be required. Nevertheless, theexemplary 2 stage amplifier scheme can be redundant and can lead tospreading of output pulses width due and reaching its saturation regiontoo often.

Alternatively, embodiments of the present invention can provide a1-stage amplifier. The exemplary 1-stage amplifier can be similar to the2-stage amplifier shown in FIG. 2, but without amplifier U57 andcapacitor C35. The resulting scheme can provide 20 dB amplification andchanged polarity of the pulse only once. After the amplifier, thepositive pulse goes to the comparator, whose reference voltage can beset from 1.2V (i.e., middle point of the amplifier) up to the amplifiedpulse height. Preferably, the threshold voltage is chosen as close tothe pulse bottom as possible while being above the noise level.According to one embodiment, a threshold voltage of 1350 mV can beprovided. The 1-stage amplifier provides less amplification, and canresult in losing some very small pulses in the noise floor, however, theloss of pulses is negligible.

According to exemplary embodiments of the present invention, theintensity of the light source and the integration time ΔT can beadjusted so that the probability of the photon registration by thesystem can be established at a desired percentage. Establishing andadjusting the intensity of the light source and the integration time ΔTcan also take into account the detection rate of the photon detectors.This probability refers to the probability of photons being registeredfor given integration intervals. For example, the intensity of the lightsource and the integration time ΔT can be adjusted so that theprobability of the photon registration by the system can be 50%. Thiswould mean that in 50% of the integration intervals, photons are notregistered. This probability can be adjusted to a desired percentage(e.g., 10%, 25%, 75%, etc.).

In operation, the FPGA can count the photons detected during ΔT andassign a corresponding the bit value. For example, a “0” can be assignedfor zero photon count and a “1” can be assigned for photon counts equalto and higher than 1. As shown in FIG. 3, exemplary TRNG 300 cantransfer the obtained sequence of “0” and “1” bits to a PC or a networkarrangement with multiple PC clients using PCI-E or a USB controller310. As shown in FIG. 3, exemplary TRNG 300 is substantially similar toexemplary TRNG 100 shown in FIG. 1. As shown in FIG. 3, the exemplarysystem 300 can include an adjustable light source 302 and a plurality ofchannels feeding into a signal processing unit (e.g., FPGA 308), whichcan output the generated random number to PCI-E or USB controller 310.Each channel can include, for example, a single photo sensor 304 andinterfacing electronics 306.

Random numbers can be generated at 8 Mbit/s per channel (256 Mbit/sectotal rate) at 4 million counts per second per channel. Further, a goodquality of the generated random sequences can be achieved.

FIG. 4 shows a block diagram 400 of two exemplary channels of a TRNGaccording to an embodiment of the present invention. As shown in FIG. 4,PMT 402 of each channel can detect photon flux of the light source andcan generate current pulses for the detected photons. The signal isamplified by amplifier 404, and the number of pulses are counted bycounter 406 during a set integration period. As described herein, theintegration interval for counter 406 can be adjusted by accumulationcounter 408, and counter 406 can output the digital output 410 (e.g.,‘0’ in instances where the counter counted zero pulses and ‘1’ ininstances where the counter counted one or more pulses). In order toobtain a truly random binary sequence, it is preferable that theprobability of ‘0’s and ‘1’s in the sequence are the same. For anyintensity of the photon flux in order to achieve 50% probability offinding zero photons (and, accordingly, the probability of finding oneor more photons) the integration interval is preferably chosen in such away, that the average number of photons in it will be 0.69314 . . . .Accordingly, for example, if the photon flux has 10 million photons persecond, the integration interval is preferably ˜69.314 ns.

However, the photon source typically is slightly unstable, and thereforethe average number of photons in the flux might change over time.Accordingly, in order to keep having 50% probability of having zerophotons in the integration interval, the interval can be adjusted inaccordance with the changes of the photon flux. To perform anadjustment, an average number of counts over a given number ofintegration intervals (e.g., 10, 100, 10000, etc.) can be determined. Ifthe average number becomes higher than necessary to produce 50% of zerophotons in the chosen integration interval, the FPGA can increase theintegration interval. If the average number becomes lower, the FPGA candecrease the integration interval. Since these variations of countsmight be different in each channel, the adjustment of the integrationinterval can be performed separately for each channel.

The individual TRNG channels of the exemplary TRNG typically produce rawstreams of random numbers. FIG. 5 shows exemplary results 500 of arandomness test of an exemplary multi-channel true random numbergenerator according to an embodiment of the present invention. Plot 502is a visual representation of random sequences generated by a 32-channelsingle photon detector according to embodiments of the presentinvention. The photon collection time ΔT is chosen so that theprobability of getting “0” counts is 50%. The distributions of theoutput random bits corrected with AMLS method shows that with anappropriate selection of the detection thresholds, photon collectiontime, and the post-processing of the obtained sequences of randomnumbers, correlated bits can be excluded, and the biased sequence can becorrected. Plot 504 is a histogram plot of the number of occurrences of8 bit integer values plotted against values, and table 506 summarizesstatistical information in connection with the exemplary randomness test(e.g., Entropy of 7.995373 bits per byte; Chi Square of 272.11 for 42364samples randomly exceeds this value 22.04% of times; mean of 127.8022;Monte Carlo value for Pi of 3.104249292 (1.19% error); and serialcorrelation coefficient of 0.008978). Accordingly, the raw streams maybe biased and the raw streams from individual channels may becorrelated. For example, single photon sensors often have channelcrosstalk (e.g., photons received by one sensor channel may triggerelectric pulses in neighboring channels—inter-channel crosstalk may beas high as 5-6%). A channel A may cause simultaneous response (falsedetection) in channel B and vice versa. The probability of thecrosstalk, P_(CROSSTALK), is a parameter of our simulation, and it mayvary from P_(CROSSTALK)=0 to P_(CROSSTALK)=1. Accordingly, in order toobtain a conditioned sensor output, the raw streams obtained fromindividual channels can be first unbiased individually, and thencombined into the TRNG output. The conditioning of the raw streamsobtained from the individual channels can remove channel crosstalk fromthe combined output of the TRNG.

In order to obtain unbiased output random sequences, the data can beconditioned by applying an unbiasing algorithm. For example, accordingto certain exemplary embodiments, consecutive bits obtained in eachindividual channel can be grouped in pairs. For example, if the bitsequence in a pair was ‘1, 0’—the output bit can be set to have thevalue ‘1’; and if the bit sequence in a pair was ‘0, 1’—the output bitcan be set to have the value ‘0’. Further, bit sequence pairs ‘1, 1’ and‘0, 0’ can be discarded. This unbiasing algorithm can yield randomsequences with 0.5 probabilities of ‘ones’ and ‘zeros’. After theconditioning of the individual channels, the unbiased data from thechannels can be combined into one output sequence. Exemplary unbiasingof exemplary Channels A and B are shown in FIG. 6. FIG. 6 shows graphs600 of exemplary random sequences generated by two channels and theconditioned output of two channels of an exemplary multi-channel truerandom number generator according to an embodiment of the presentinvention according to an embodiment of the present invention.Specifically, plots 602 and 604 show random sequences generated byChannels A and B having channel cross talk, and plots 606 and 608 showthe unbiased sequences obtained after conditioning.

The embodiments and examples shown above are illustrative, and manyvariations can be introduced to them without departing from the spiritof the disclosure. For example, elements and/or features of differentillustrative and exemplary embodiments herein may be combined with eachother and/or substituted with each other within the scope of thedisclosure. For a better understanding of the disclosure, referenceshould be had to any accompanying drawings and descriptive matter inwhich there is illustrated exemplary embodiments of the presentinvention.

TABLE 1 Exemplary Simulated Crosstalk with 1 μs Detection IntegrationWindow Exemplary Exemplary Exemplary Serial Optimum Exemplary MonteCarlo correlation Exemplary compression Exemplary Chi square Arithmeticvalue for π coefficient Entropy (% of distribution mean (value for π(totally (bits per the size (value and percentage of (127.5- and erroruncorrelated- byte) reduction) times it exceeds this value) random)percent) 0.0) Channel A, 7.999376 by 0% 270.42, and randomly would127.6604 3.143559413 −0.000972 UNBIASED exceed this value 24.23 (error0.06 percent of the times. percent). Channel B, 7.999332 by 0% 279.43,and randomly would 127.4494 3.138242431   0.001868 Conditioned exceedthis value 14.04 (error 0.11 percent of the times. percent). Channel Awith 7.999444 by 0% 240.79, and randomly would 127.6508 3.149310829−0.002766 crosstalk from exceed this value 72.96 (error 0.25 channel B,percent of the times. percent). Conditioned Channel B 7.999365 by 0%263.20, and randomly would 127.4461 3.145796979   0.003366 withcrosstalk exceed this value less than (error 0.13 from channel A, 34.87percent of the times. percent). Conditioned Channels A and 7.999695 by0% 254.40, and randomly would 127.6106 3.140311095 −0.001760 B, Combinedexceed this value less than (error 0.04 after 49.87 percent of thetimes. percent). conditioning Channel A and 7.999695 by 0% 252.39, andrandomly would 127.5488 3.145997553 −0.000682 B exceed this value lessthan (error 0.14 Crosstalk 5%, 53.45 percent of the times. percent).Combined after conditioning Channel A and 7.999695 by 0% 242.64, andrandomly would 127.5402 3.138168099 −0.000699 B exceed this value lessthan (error 0.11 Crosstalk 25%, 70.09 percent of the times. percent).Combined after conditioning Channel A and 7.999600 by 0% 294.02, andrandomly would 127.3877 3.146863301   0.000414 B exceed this value lessthan (error 0.17 Crosstalk 50%, 4.69 percent of the times. percent).Combined after conditioning Channel A and 7.999512 by 0% 274.02, andrandomly would 127.3272 3.137733757   0.000951 B exceed this value lessthan (error 0.12 Crosstalk 99%, 19.73 percent of the times. percent).Combined after conditioning Channel A and 3.999966 by 0% 6051877.50, andrandomly 127.8406 2.712949330 −0.000770 B would exceed this value less(error 13.64 Crosstalk than 0.01 percent of the percent). 100%, times.Combined after conditioning Channel A and 7.810750 by 2% 182315.35, andrandomly 150.5737 2.505238068   0.002374 B combined would exceed thisvalue less (error 20.26 before than 0.01 percent of the percent).conditioning, times. 0% crosstalk Channel A and 7.810694 by 2%173644.52, and randomly 150.4988 2.506399037   0.001898 B combined wouldexceed this value less (error 20.22 before than 0.01 percent of thepercent). conditioning, times. 5% crosstalk

TABLE 2 Exemplary Experimental Results with 125 ns Detection IntegrationWindow Exemplary Exemplary Exemplary Serial Optimum Exemplary MonteCarlo correlation Exemplary compression Exemplary Chi square Arithmeticvalue for π coefficient Entropy (% of distribution mean (value for(totally (bits per the size (value and percentage of (127.5- π and erroruncorrelated- byte) reduction) times it exceeds this value) random)percent) 0.0) Channel 5 7.999923 by 0% 234.00, and randomly would127.4309 3.143115877   0.000337 exceed this value 82.30 (error 0.05percent of the times. percent). Channel 7 7.999946 by 0% 242.64, andrandomly would 127.4957 3.139915073 −0.000201 exceed this value 70.09(error 0.05 percent of the times. percent). Channel 9 7.999919 by 0%295.26, and randomly would 127.4969 3.139586549 −0.000153 exceed thisvalue 4.22 (error 0.06 percent of the times. percent). Channel 5 and7.999961 by 0% 290.13, and randomly would 127.4688 3.141796309 −0.0004907 combined. exceed this value less than (error 0.01 6.44 percent of thetimes. percent). Channels 5 7.999954 by 0% 304.32, and randomly would127.4172 3.142264587 −0.000483 and 9 exceed this value less than (error0.02 combined. 1.85 percent of the times. percent). Channel 7 and7.999969 by 0% 252.40, and randomly would  7.999969 3.142209850−0.000056 9 combined. exceed this value less than (error 0.02 53.42percent of the times. percent).

TABLE 3 Exemplary Experimental Results with 125 nsec DetectionIntegration Window with Three Neighboring Channels (e.g., channels 6, 7,and 8). Exemplary Exemplary Exemplary Serial Optimum Exemplary MonteCarlo correlation Exemplary compression Exemplary Chi square Arithmeticvalue for π coefficient Entropy (% of distribution mean (value for (iftotally (bits per the size (value and percentage of (127.5- π and erroruncorrelated = byte) reduction) times it exceeds this value) random)percent) 0.0) Channel 6 7.999922 by 0% 251.33, and randomly 127.57383.137425890   0.000313 would exceed this value (error 0.13 55.33 percentof the percent). times. Channel 7 7.999946 by 0% 242.64, and randomly127.4957 3.139915073 −0.000201 would exceed this value (error 0.05 70.09percent of the percent). times. Channel 8 7.999937 by 0% 282.00, andrandomly 127.5098 3.143120281 −0.000533 would exceed this value (error0.05 11.80 percent of the percent). times. Channels 6 and 7.999966 by 0%261.28, and randomly 127.5091 3.138306258   0.000270 7 combined. wouldexceed this value (error 0.10 less than 38.00 percent percent). of thetimes. Channels 7 and 7.999969 by 0% 274.10, and randomly 127.51063.139564689   0.000222 8 combined. would exceed this value (error 0.06less than 19.63 percent percent). of the times. Channels 6, 7 7.999979by 0% 252.92, and randomly 127.5016 3.140885280 −0.000356 and 8 wouldexceed this value (error 0.02 combined. less than 47.20 percentpercent). of the times.

What is claimed is:
 1. A true random number generator comprising: alight source configured to produce randomly distributed photons; aplurality of detection channels configured to receive the randomlydistributed photons produced by the light source, each detection channelincluding a photon, sensor configured to detect a receipt of at leastone photon during successive integration time-periods set for acorresponding detection channel amongst the plurality of detectionchannels, and generate an output signal for each integration time-periodamongst the successive integration time-periods set for thecorresponding detection channel, the output signal being assigned afirst value in a case that at least one photon was not received duringthe integration time-period and being assigned a second value in a casethat at least one photon was received during the integrationtime-period; a signal processing unit configured to (i) condition theoutput signal of each of the plurality of detection channels andgenerate a conditioned output signal for each of the plurality ofdetection channels and (ii) combine the conditioned output signals andgenerate a true random number based on the combination of theconditioned output signals.
 2. The true random number generator of claim1, wherein the light source includes a Lambertian light source.
 3. Thetrue random number generator of claim 1, wherein the successiveintegration time-periods are a function of a photon detection rate ofthe photon sensor.
 4. The true random number generator of claim 1,wherein the signal processing unit is configured to minimize cross-talkbetween the plurality of detection channels.
 5. The true random numbergenerator of claim 1, wherein the signal processing unit is configuredto compare successive values in conditioning the output signal of eachof the plurality of detection channels.
 6. The true random numbergenerator of claim 5, wherein the comparison of successive valuesincludes comparing whether successive values are equal.
 7. The truerandom number generator of claim 6, wherein conditioning the outputsignal includes discarding the values when the successive values areequal and adopting the first of the successive values when thesuccessive values are different.
 8. The true random number generator ofclaim 1, wherein the signal processing unit is configured to combine theconditioned output signals sequentially in producing the true randomnumber.
 9. The true random number generator of claim 1, wherein thelight source and the integration time-periods are configured such that aprobability of receiving at least one photon during each integrationtime-period is approximately one-half.
 10. The true random numbergenerator of claim 1, wherein the true random number generator includes32 detection channels.
 11. The true random number generator of claim 1,wherein the true random number generator includes more than 10 detectionchannels.
 12. The true random number generator of claim 1, wherein thetrue random number generator includes more than 30 detection channels.13. The true random number generator of claim 1, wherein the true randomnumber generator includes more than 100 detection channels.
 14. The truerandom number generator of claim 1, wherein the true random numbergenerator provides a throughput of at least 100 Mbit/s.
 15. The truerandom number generator of claim 1, wherein the true random numbergenerator provides a throughput of at least 1 Gbit/s.
 16. The truerandom number generator of claim 1, wherein the true random numbergenerator provides a throughput of at least 10 Gbit/s.
 17. The truerandom number generator of claim 1, wherein the true random numbergenerator provides a throughput of at least 100 Gbit/s.
 18. A method forrequiring a true random number, comprising obtaining a random numbergenerated by the true random number generator of claim
 1. 19. The truerandom number generator of claim 1, wherein adjustment of an integrationtime-period for a first detection channel, amongst the plurality ofdetection channels, is adjusted separately from an integrationtime-period for a second detection channel, amongst the plurality ofdetection channels, based on photon detection rates of photon sensors ofsaid first and second detection channels, respectively, over apredetermined number of integration time-periods.
 20. A method forgenerating true random numbers, the method comprising: providing a lightsource configured to produce randomly distributed photons; receiving therandomly distributed photons produced by the light source using aplurality of detection channels which include a plurality of photonsensors, respectively; detecting, using a photon sensor amongst theplurality of photon sensors, a receipt of at least one photon duringsuccessive integration time-periods set for a corresponding detectionchannel amongst the plurality of detection channels; generating anoutput signal for each integration time-period amongst the successiveintegration time-periods set for the corresponding detection channel,the output signal being assigned a first value in a case that at leastone photon was not received during the integration time-period and beingassigned a second value in a case that at least one photon was receivedduring the integration time-period; conditioning the output signal ofeach of the plurality of detection channels and generating a conditionedoutput signal for each of the plurality of detection channels; andcombining the conditioned output signals and generating a true randomnumber based on the combination of the conditioned output signals usinga signal processing unit.